Overview

Salmon leaping at Willamette Falls from NOAA’s Historic Fisheries Collection. Unknown photographer, 27 June 1950.

Our overview goes here; includes

Hydroelectric power represents an important renewable and low-emissions energy source, but the construction and development of the water source that the power plants can require sometimes threatens resident fish populations. Willamette Falls, located outside of Portland in northwestern Oregon (see map below), is an important traditional Native American fishing ground. To protect this natural resource and aid the passage of salmon and steelhead runs over the falls, fishways have been constructed and updated over time. Daily fish counts are monitored to ensure that migration of these fish populations continues to be unhindered by the power plant and the falls. This project summarizes findings from studying Willamette Falls monitoring data from 2001 to 2010.

Minnie Edits:

Hydroelectric power represents an important renewable and low-emissions energy source, but the construction and development of the water source that these plants can require sometimes threatens resident fish populations. Fishways have been constructed and updated over time at Willamette Falls to aid the passage of salmon and steelhead runs over the falls. Daily fish counts are monitored to ensure that migration of these fish populations continues to be unhindered by the power plant and the falls. This project summarizes findings from studying Willamette Falls monitoring data from 2001 to 2010.

Data were shared by and accessed from Columbia River DART:
Columbia River DART (Data Access in Real Time), Columbia Basin Research, University of Washington
Accessed Feb 1, 2021 at http://www.cbr.washington.edu/dart/query/adult_graph_text.

world <- ne_countries(scale = "medium", returnclass = "sf") # pull world data
states <- st_as_sf(map("state", plot = FALSE, fill = TRUE)) # pull state data
states <- cbind(states, st_coordinates(st_centroid(states))) # project

falls <- data.frame(longitude = c(-122.61763), latitude = c(45.35239)) %>% # make point
  st_as_sf(coords = c("longitude", "latitude"), # as sf
           crs = 4326, agr = "constant")

ggplot(data = world) + # plot
  geom_sf(fill = "antiquewhite") +
  geom_sf(data = states, fill = "peachpuff3") + # add state outlines and fill
  geom_sf(data = falls, size = 4, shape = 23, fill = "royalblue3") + # add falls loc
  coord_sf(xlim = c(-125, -110), ylim = c(40, 50), expand = FALSE) + # set bounding
  theme_minimal() + # minimal theme
  labs(title = "Willamette Falls location", # add labs
       subtitle = "2001 - 2010",
       caption = "Bri Baker, 2021")

willamette_salmon <- read_csv(here("data", "willamette_fish_passage.csv")) %>%  # read in data
  clean_names() %>% # names in tidy format
  select(date, coho, jack_coho, steelhead) %>% # select desired species
  mutate(date = mdy(date)) %>%  # make date class
   as_tsibble(key = NULL, index = date) # convert to tsibble

salmon_longer <- willamette_salmon %>%  
  replace(is.na(.), 0) %>%  # replace na with 0
  pivot_longer(coho:steelhead, # consolidate species to one column
               names_to = "species",
               values_to = "counts")

Original time series

<<<<<<< HEAD 
# Bri, I am saving your df if that's ok - so I can use it later on!
salmon_longer <- willamette_salmon %>%  
  replace(is.na(.), 0) %>%  # replace na with 0
  pivot_longer(coho:steelhead, # consolidate species to one column
               names_to = "species",
               values_to = "counts")

#ggplot(data = salmon_longer, aes(x = date, y = counts, color = species)) + # make ggplot
willamette_salmon %>%  
  replace(is.na(.), 0) %>%  # replace na with 0
  pivot_longer(coho:steelhead, # consolidate species to one column
               names_to = "species",
               values_to = "counts") %>% 
  ggplot(aes(x = date, y = counts, color = species)) + # make ggplot
  geom_line() + #as a lineplot
  labs(x = "Year", # add labs
       y = "Count",
       title = "Salmon counts at Willamette Falls fish passage",
       subtitle = "2001 - 2010",
       caption = "Bri Baker, 2021\nSource: Columbia River DART",
       color = "Species") +
  scale_color_manual(labels = c("Coho", "Jack Coho", "Steelhead"), # change legend names
                     values = c("aquamarine3", "cornflowerblue", "goldenrod1")) + # change colors
  scale_x_date(date_breaks = "1 year", # show all years
               date_labels = "%Y") +
  theme_minimal() + # use theme_minimal
  theme(legend.position = c(0.15, 0.75), # move legend
        legend.background = element_rect(fill="white", linetype = "solid", color = "whitesmoke"), # format legend
        axis.text.x = element_text(angle = 30, vjust = 1, hjust = 1)) # angle x labels

======= 
  ggplot(salmon_longer, aes(x = date, y = counts, color = species)) + # make ggplot
  geom_line() + #as a lineplot
  labs(x = "Year", # add labs
       y = "Count",
       title = "Salmon counts at Willamette Falls fish passage",
       subtitle = "2001 - 2010",
       caption = "Bri Baker, 2021\nSource: Columbia River DART",
       color = "Species") +
  scale_color_manual(labels = c("Coho", "Jack Coho", "Steelhead"), # change legend names
                     values = c("aquamarine3", "cornflowerblue", "goldenrod1")) + # change colors
  scale_x_date(date_breaks = "1 year", # show all years
               date_labels = "%Y") +
  theme_minimal() + # use theme_minimal
  theme(legend.position = c(0.15, 0.75), # move legend
        legend.background = element_rect(fill="white", linetype = "solid", color = "whitesmoke"), # format legend
        axis.text.x = element_text(angle = 30, vjust = 1, hjust = 1)) # angle x labels

>>>>>>> bri-baker-branch

Takeaways

  • The most apparent pattern in the data is seasonality on an annual basis, with peak sightings occurring mid-year(?).
  • The bulk of Steelhead movement occurs earlier in the year than that of Coho and Jack Coho.
  • Coho increased in abundance over the time studied, particularly in 2009 and 2010.

Minnie Edits:

  • The bulk of Steelhead movement occurs earlier in the year than that of Coho and Jack Coho
  • Coho increased in abundance over the time studied, particularly in 2009 and 2010.

Seasonplots – Also ugly and unfinished

<<<<<<< HEAD 
salmon_season <-  willamette_salmon %>%
  rename(Coho = coho,
         "Jack Coho" = jack_coho,
         Steelhead = steelhead) %>% 
  pivot_longer(Coho:Steelhead, 
               names_to = "species",
               values_to = "counts") 
salmon_season %>% 
  gg_season(counts)

coho_subset <- willamette_salmon %>% 
  select(date, coho) 
coho_subset %>% 
  gg_season(coho)

jack_coho_subset <- willamette_salmon %>% 
  select(date, jack_coho) 
jack_coho_subset %>% 
  gg_season(jack_coho)

steelhead_subset <- willamette_salmon %>% 
  select(date, steelhead) 
steelhead_subset %>% 
  gg_season(steelhead)

Annual counts by species

Issues I’m having: * I think the “year” variable I’m using is not a date type? Bri’s code for formatting the x-axis breaks isn’t working for me. * How to get the facets labeled with the proper names of the species?

# Using Bri's "pivot_longer-ed" df:
annual <- salmon_longer %>%
  index_by(yr = ~year(.)) %>% # index by year
  group_by(yr, species) %>% 
  summarize(annual_count = sum(counts)) # sum counts by year and species

labels <- c("Coho", "Jack Coho", "Steelhead") # capitalize

# Using Bri's graph formatting:
ggplot(data = annual, aes(x = yr, y = annual_count, fill = species)) + # make ggplot
  geom_col() + #as a barchart
  labs(x = "Year", # add labs
       y = "Count",
       title = "Salmon counts at Willamette Falls fish passage by year",
       subtitle = "2001 - 2010",
       caption = "Minnie Ringland, 2021\nSource: Columbia River DART") +
  scale_fill_manual(values = c("aquamarine3", "cornflowerblue", "goldenrod1")) + # change colors
  scale_x_discrete(breaks = 2001:2011) +
  #(date_breaks = "1 year", # show all years #date_labels = "%Y") +
  theme_minimal() + # use theme_minimal
  theme(legend.position = "none", # faceted so no legend
        axis.text.x = element_text(angle = 30, vjust = 1, hjust = 1)) + # angle x labels
  facet_wrap(~species)#,labeller=labeller(species = labels))

Takeaways

  • Once we collapse the data by year, there do not appear to be any common trends in the data across species.
  • As seen in the original time series, Steelhead were sighted in greater numbers than the two Coho species across the entire study period, suggesting that the population is more abundant or that these fish are easier to count.
  • However, Coho fish were seen in greater abundance, particularly in the last two years of the study period, which could suggest a population increase. Jack Coho fish seem to be present in very low numbers, which could be cause for concern.
======= 
salmon_season <-  willamette_salmon %>%
  rename(Coho = coho,
         "Jack Coho" = jack_coho,
         Steelhead = steelhead) %>% 
  pivot_longer(Coho:Steelhead, 
               names_to = "species",
               values_to = "counts") 

salmon_season %>% 
  gg_season(counts)

coho_subset <- willamette_salmon %>% 
  select(date, coho) 
coho_subset %>% 
  gg_season(coho)

jack_coho_subset <- willamette_salmon %>% 
  select(date, jack_coho) 
jack_coho_subset %>% 
  gg_season(jack_coho)

steelhead_subset <- willamette_salmon %>% 
  select(date, steelhead) 
steelhead_subset %>% 
  gg_season(steelhead)

gg_subseries(salmon_longer, period = "day")

Annual counts by species

Issues I’m having: * I think the “year” variable I’m using is not a date type? Bri’s code for formatting the x-axis breaks isn’t working for me.; yeah its not of date class; i converted it, but what you had also works (just remove the mutate) * How to get the facets labeled with the proper names of the species?

# Using Bri's "pivot_longer-ed" df:
annual <- salmon_longer %>%
  group_by_key() %>% # group by sp
  index_by(yr = ~year(.)) %>% # index by year
  summarize(annual_count = sum(counts)) %>% # sum counts by year and species
  mutate(yr = as.Date(ISOdate(yr, 1, 1)))

species_labs <- c("Coho", "Jack Coho", "Steelhead") # capitalize

# Using Bri's graph formatting:
ggplot(data = annual, aes(x = yr, y = annual_count, fill = species)) + # make ggplot
  geom_col() + #as a barchart
  labs(x = "Year", # add labs
       y = "Count",
       title = "Salmon counts at Willamette Falls fish passage by year",
       subtitle = "2001 - 2010",
       caption = "Minnie Ringland, 2021\nSource: Columbia River DART") +
  scale_fill_manual(values = c("aquamarine3", "cornflowerblue", "goldenrod1")) + # change colors
  #scale_x_discrete(breaks = 2001:2011) +
  scale_x_date(date_breaks = "1 year", # show all years #
   date_labels = "%Y") +
  theme_minimal() + # use theme_minimal
  theme(legend.position = "none", # faceted so no legend
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) + # angle x labels
  facet_wrap(~species)

             #labeller = labeller(species = species_labs))

Takeaways

  • Once we collapse the data by year, there do not appear to be any common trends in the data across species.
  • As seen in the original time series, Steelhead were sighted in greater numbers than the two Coho species across the entire study period, suggesting that the population is more abundant or that these fish are easier to count.
  • However, Coho fish were seen in greater abundance, particularly in the last two years of the study period, which could suggest a population increase. Jack Coho fish seem to be present in very low numbers, which could be cause for concern.
>>>>>>> bri-baker-branch